EP4174458A1 - Procédé et système d'évaluation plus rapide de la vitesse du son dans des fluides à l'aide d'une technique de détection de compression - Google Patents

Procédé et système d'évaluation plus rapide de la vitesse du son dans des fluides à l'aide d'une technique de détection de compression Download PDF

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Publication number
EP4174458A1
EP4174458A1 EP22197467.8A EP22197467A EP4174458A1 EP 4174458 A1 EP4174458 A1 EP 4174458A1 EP 22197467 A EP22197467 A EP 22197467A EP 4174458 A1 EP4174458 A1 EP 4174458A1
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EP
European Patent Office
Prior art keywords
sound speed
pulse
hardware processors
assessment system
system via
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EP22197467.8A
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German (de)
English (en)
Inventor
Arijit Sinharay
Raj RAKSHIT
Supriya Gain
Tapas Chakravarty
Subhadeep Basu
Achanna Anil Kumar
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Tata Consultancy Services Ltd
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Tata Consultancy Services Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/02—Analysing fluids
    • G01N29/024—Analysing fluids by measuring propagation velocity or propagation time of acoustic waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H5/00—Measuring propagation velocity of ultrasonic, sonic or infrasonic waves, e.g. of pressure waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/34—Generating the ultrasonic, sonic or infrasonic waves, e.g. electronic circuits specially adapted therefor
    • G01N29/348—Generating the ultrasonic, sonic or infrasonic waves, e.g. electronic circuits specially adapted therefor with frequency characteristics, e.g. single frequency signals, chirp signals
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/4472—Mathematical theories or simulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/46—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by spectral analysis, e.g. Fourier analysis or wavelet analysis

Definitions

  • the disclosure herein generally relates to fluid quality estimation, and, more particularly, to a method and a system for faster assessment of sound speed in fluids using compressive sensing technique.
  • Fluid quality assessment is a key requirement for many industries. Acoustic property like sound speed in fluid serves as a reliable marker for assessing the quality of the fluid as aging or adulteration would alter the sound speed in the fluid instantly.
  • acoustic interferometry also referred as Swept Frequency Acoustic Interferometry (SFAI)
  • SFAI Swept Frequency Acoustic Interferometry
  • SFAI technique probes the fluid kept in the container at different frequencies over a wide frequency range and records the response of the liquid.
  • the SFAI uses a narrowband filtering to significantly enhance the signal quantity as compared to traditional pulse-echo based measurements.
  • the measurement using the SFAI technique is relatively slow as there is a need to sweep a wide range of frequencies. Further, for each probing frequency, there exist some settling time that is governed by a narrow band filter to avoid recording transients, thereby putting a fundamental limit on how fast the frequency sweep can be performed. Though processing of the data can be made arbitrarily fast, one cannot take the frequency sweep and record the response arbitrarily fast as this is tied with physical/fundamental limits posed by the required measurement resolution and filter bandwidth as mentioned earlier.
  • Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, there is provided a processor implemented method for faster assessment of sound speed in fluids using compressive sensing technique.
  • the method comprises receiving by a sound speed assessment system via one or more hardware processors, one or more excitation signals and one or more quadrature signals generated corresponding to the one or more excitation signals from a fluid container containing a fluid; creating, by the sound speed assessment system via one or more hardware processors, a pseudo analytic signal vector based, at least in part, on the one or more excitation signals and the one or more quadrature signals using a pre-defined vector creation formula; estimating, by the sound speed assessment system via one or more hardware processors, a pulse-echo view by applying a compressive sensing technique over the created pseudo analytic signal vector, wherein the pulse-echo view is a vector; determining, by the sound speed assessment system via one or more hardware processors, whether the estimation of the pulse-echo view is successful based on a pre-defined criteria; and calculating, by the sound speed assessment system via one or more hardware processors, a sound speed in the fluid based on the determination using the pulse-echo view and a pre-defined sound speed
  • a sound speed assessment system for faster assessment of sound speed in fluids using compressive sensing technique.
  • the system comprises a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: receive one or more excitation signals and one or more quadrature signals generated corresponding to the one or more excitation signals from a fluid container containing a fluid; create a pseudo analytic signal vector based, at least in part, on the one or more excitation signals and the one or more quadrature signals using a pre-defined vector creation formula; estimate a pulse-echo view by applying a compressive sensing technique over the created pseudo analytic signal vector, wherein the pulse-echo view is a vector; determine whether the estimation of the pulse-echo view is successful based on a pre-defined criteria; and calculate a sound speed in the fluid based on the determination using the pulse-echo view and a pre-defined sound speed
  • one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause a method for faster assessment of sound speed in fluids using compressive sensing technique.
  • the method comprises receiving, by a sound speed assessment system via one or more hardware processors, one or more excitation signals and one or more quadrature signals generated corresponding to the one or more excitation signals from a fluid container containing a fluid; creating, by the sound speed assessment system via one or more hardware processors, a pseudo analytic signal vector based, at least in part, on the one or more excitation signals and the one or more quadrature signals using a pre-defined vector creation formula; estimating, by the sound speed assessment system via one or more hardware processors, a pulse-echo view by applying a compressive sensing technique over the created pseudo analytic signal vector, wherein the pulse-echo view is a vector; determining, by the sound speed assessment system via one or more hardware processors, whether the estimation of the pulse-e
  • Swept Frequency Acoustic Interferometry is a well-known noninvasive technique for taking measurements of fluid's acoustic parameters like sound speed in fluid, sound attenuation, and density of fluid as these parameters help in fluid characterization or in other case assessing the quality of the fluid.
  • the fluid can be considered as adulterated.
  • Embodiments of the present disclosure overcome the above-mentioned disadvantages, such as slower measurement, higher computational expense, etc., by providing a system and a method for faster assessment of sound speed in fluids using compressive sensing technique. More specifically, the system and the method of the present disclosure follow a compressive sensing technique for achieving faster SFAI measurement that further helps in quicker determination of sound speed in the fluid. Basically, the system and the method help in quicker characterization of fluids while producing less amount of raw data, thereby making it helpful in applications where available resources are minimum. For example, in gas and oil pipelines where most of the installed sensors are battery powered, the collected data is either saved on a local memory unit or transmitted to a central server for post processing. In these scenarios, less amount of raw data would be really helpful in recording the data for longer time while also saving battery power in transmission time.
  • phase sensitive measurement is taken to get excitation and quadrature components.
  • excitation component ( I ) response of a frequency excitation is multiplied with the excitation frequency itself and then lowpass filtering is performed.
  • quadrature component ( Q ) is obtained by first multiplying a 90-phase shifted version of the excitation signal with the response at the same time and then performing lowpass filtering.
  • Magnitude vs Frequency plot is generated to form the required resonance or SFAI spectra that further helps in determining sound speed and sound attenuation information as discussed previously.
  • the SFAI spectra that is generated is in frequency domain but as per the prior knowledge, the same SFAI spectra can also be transformed to time-domain (hereinafter referred as Transformed Time Domain (TTD)) and equivalent pulse-echo pictures can be generated from the same I , Q measurement data.
  • TTD Transformed Time Domain
  • the Fourier Transform on Z c is taken for automatically transforming the data in time domain.
  • the Fourier transform on Z c provides a TTD view that is similar to a straightforward pulse-echo type measurement where a transmitted pulse from transmitter piezo Tx goes through a plurality of paths due to reflections/transmission at interface boundaries to arrive at receiver piezo Rx.
  • unique shortest path(s) with corresponding time of arrivals can be determined by looking at a pulse propagation diagram created corresponding to the TTD view.
  • An example representation of a pulse propagation diagram created corresponding to a TTD view is shown with reference to FIG. 1 .
  • v liquid 2 ⁇ D T 2 ⁇ T 1
  • the system ensures faster assessment of sound speed in liquid by providing a sound speed assessment system (explained in detail with reference to FIGS. 2 and 3 ) that calculates the sound speed using some random samples instead of all the samples that are provided in a defined frequency scanning range.
  • the sound speed assessment system first creates a pseudo analytic signal vector based on excitation signals and corresponding quadrature signals using a pre-defined vector creation formula.
  • the excitation signals and the corresponding quadrature signals are obtained by exciting frequencies associated with some random samples selected from a plurality of samples available in a defined frequency scanning range of a full frequency sweep signal.
  • the sound speed assessment system creates a pulse-echo view by applying a compressive sensing technique over the created pseudo analytic signal vector.
  • the sound speed assessment system calculates sound speed in the fluid using the pulse-echo view and the pre-defined sound speed calculation formula.
  • FIGS. 1 through 7B where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
  • FIG. 2 illustrates an exemplary representation of an environment 200 related to at least some example embodiments of the present disclosure.
  • the environment 200 is presented in one arrangement, other embodiments may include the parts of the environment 200 (or other parts) arranged otherwise depending on, for example, selecting random samples, exciting frequencies, etc.
  • the environment 200 generally includes a fluid container 202 containing and a sound speed assessment system 206, each coupled to, and in communication with (and/or with access to) a network 204.
  • the network 204 may include, without limitation, a light fidelity (Li-Fi) network, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a satellite network, the Internet, a fiber optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, a virtual network, and/or another suitable public and/or private network capable of supporting communication among two or more of the parts or users illustrated in FIG. 1 , or any combination thereof.
  • Li-Fi light fidelity
  • LAN local area network
  • WAN wide area network
  • MAN metropolitan area network
  • satellite network the Internet
  • a fiber optic network a coaxial cable network
  • IR infrared
  • RF radio frequency
  • Various entities in the environment 200 may connect to the network 104 in accordance with various wired and wireless communication protocols, such as Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), 2nd Generation (2G), 3rd Generation (3G), 4th Generation (4G), 5th Generation (5G) communication protocols, Long Term Evolution (LTE) communication protocols, or any combination thereof.
  • TCP/IP Transmission Control Protocol and Internet Protocol
  • UDP User Datagram Protocol
  • 2G 2nd Generation
  • 3G 3rd Generation
  • 4G 4th Generation
  • 5G 5th Generation
  • LTE Long Term Evolution
  • the fluid container 202 is of a regular geometry and contains fluid.
  • the fluid container 202 is a rectangular box with ' D ' as inner wall-to-wall distance and 'd' as wall thickness.
  • the fluid container 202 is also equipped with one or more piezo electric transducers that are used for exciting the fluid kept in the fluid container 202.
  • the piezo electric transducers present in the fluid container 202 are configured to generate one or more excitation signals and one or more corresponding quadrature signals by exciting the fluid with one or more frequencies associated with one or more random samples.
  • the one or more random samples are selected randomly from a plurality of samples available in a defined frequency scanning range of a full frequency sweep signal with defined frequency increment steps.
  • the generated one or more excitation signals and the one or more corresponding quadrature signals are shared with the sound speed assessment system 206 using the network 204.
  • the sound speed assessment system 206 includes one or more hardware processors, such as hardware processors 304 and a memory, such as memory 302.
  • the sound speed assessment system 206 is configured to perform one or more of the operations described herein.
  • the sound speed assessment system 206 is configured to receive the one or more excitation signals and the one or more corresponding quadrature signals via the network 204 from the fluid container 202 containing the fluid.
  • the sound speed assessment system 206 is then configured to create a pseudo analytic signal vector based, at least in part, on the one or more excitation signals and the one or more corresponding quadrature signals using a pre-defined vector creation formula. Once the pseudo analytic signal vector is created, the sound speed assessment system 206 is configured to estimate a pulse-echo view by applying a compressive sensing technique over the created pseudo analytic signal vector.
  • the sound speed assessment system 106 calculates the sound speed in the fluid based on the estimated pulse-echo view using the pre-defined sound speed calculation formula.
  • FIG. 2 The number and arrangement of systems, containers, and/or networks shown in FIG. 2 are provided as an example. There may be additional systems, containers, and/or networks; fewer systems, containers, and/or networks; different systems, containers, and/or networks; and/or differently arranged systems, containers, and/or networks than those shown in FIG. 2 . Furthermore, two or more systems shown in FIG. 2 may be implemented within a single system or device, or a single system or device shown in FIG. 2 may be implemented as multiple, distributed systems or devices. Additionally, or alternatively, a set of systems (e.g., one or more systems) of the environment 200 may perform one or more functions described as being performed by another set of systems of the environment 200 (e.g., refer scenarios described above).
  • a set of systems e.g., one or more systems of the environment 200 may perform one or more functions described as being performed by another set of systems of the environment 200 (e.g., refer scenarios described above).
  • FIG. 3 illustrates an exemplary block diagram of a sound speed assessment system 300 for faster assessment of sound speed in fluids using compressive sensing technique, in accordance with an embodiment of the present disclosure.
  • the sound speed assessment system 300 may also be referred as a system and may be interchangeably used herein.
  • the system 300 is similar to the sound speed assessment system 206 explained with reference to FIG. 1 .
  • the system 300 is embodied as a cloud-based and/or SaaS-based (software as a service) architecture.
  • the system 300 may be implemented in a server system.
  • the system 300 may be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, and the like.
  • the system 300 includes one or more processors 304, communication interface device(s) or input/output (I/O) interface(s) 306, and one or more data storage devices or memory 302 operatively coupled to the one or more processors 304.
  • the one or more processors 304 may be one or more software processing modules and/or hardware processors.
  • the hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions.
  • the processor(s) is configured to fetch and execute computer-readable instructions stored in the memory.
  • the I/O interface device(s) 306 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite.
  • the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
  • the memory 302 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
  • volatile memory such as static random access memory (SRAM) and dynamic random access memory (DRAM)
  • non-volatile memory such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
  • ROM read only memory
  • erasable programmable ROM erasable programmable ROM
  • flash memories such as hard disks, optical disks, and magnetic tapes.
  • ROM read only memory
  • erasable programmable ROM such as erasable programmable ROM
  • flash memories such as hard disks, optical disks, and magnetic tapes.
  • ROM read only memory
  • the memory 302 may store information about random frequencies that are selected, one or more algorithms for creation of matrices, such as Inverse Fast Fourier transform (IFFT) basis matrix, sensing matrix etc., pre-defined formulas, and the like.
  • the memory 302 further comprises (or may further comprise) information pertaining to input(s)/output(s) of each step performed by the system and method of the present disclosure.
  • input(s) fed at each step and output(s) generated at each step are comprised in the memory 302 and can be utilized in further processing and analysis.
  • FIG. 4 illustrates an exemplary flow diagram of a method 400 for faster assessment of sound speed in fluids using compressive sensing technique, in accordance with an embodiment of the present disclosure.
  • the method 400 may use the system 300 of FIG. 3 and sound speed assessment system 206 of FIG. 2 for execution.
  • the system 300 comprises one or more data storage devices or the memory 302 operatively coupled to the one or more hardware processors 304 and is configured to store instructions for execution of steps of the method 400 by the one or more hardware processors 304.
  • the sequence of steps of the flow diagram may not be necessarily executed in the same order as they are presented.
  • one or more steps may be grouped together and performed in form of a single step, or one step may have several sub-steps that may be performed in parallel or in sequential manner.
  • the steps of the method 400 of the present disclosure will now be explained with reference to the components of the system 300 as depicted in FIG. 3 , and the flow diagram.
  • the one or more hardware processors 304 comprised in the system 300 receive one or more excitation signals and one or more quadrature signals generated corresponding to the one or more excitation signals from a fluid container, such as fluid container 102 containing a fluid.
  • a fluid container such as fluid container 102 containing a fluid.
  • the fluid includes, but are not limited to, milk, oil, water, gasoline, alcohol etc.
  • the fluid kept in the container is excited with a continuous wave and its frequency is stepped through in time over a frequency range for creating resonance in the standard SFAI technique.
  • one or more random samples are selected from a plurality of samples available in a defined frequency scanning range of a full frequency sweep signal that is used to excite the fluid.
  • a start ( f start ) and a stop ( f stop ) frequency scanning range of a frequency sweep signal is defined.
  • the calculation of the number of frequency probing points helps in identification of the number of frequency points that need to be excited for creating the resonance condition.
  • N number of intended frequency measurement points P i.e., random samples that are to be selected are defined among the N frequency probing points such that P is very smaller than N.
  • a numeric array F is created from the f start till f stop with a gap of ⁇ steps. For example, if f start is defined as 2MHz, f stop is defined as 50 MHz, and ⁇ is defined as 0.5, then F may contain values such as ⁇ 2, 2.5, 3, 3.5...,50 ⁇ . Thereafter, P number of random samples are selected from F and the frequency values of P random samples are also noted in an array F r .
  • the frequencies available in F r of P random samples are excited sequentially to generate the one or more excitation signals (i.e., I values) and the one or more quadrature signals (i.e., Q values) generated corresponding to the one or more excitation signals.
  • the generated I and Q are then shared with the system 300 using the network 104.
  • the one or more hardware processors 304 of the system 300 create a pseudo analytic signal vector based, at least in part, on the one or more excitation signals and the one or more quadrature signals using a pre-defined vector creation formula.
  • Z r is a small subset of Z c as Z r contains some random measurements by exciting frequencies from Fr, whereas Z c is representative of high resolution standard SFAI measurement.
  • the pre-defined vector creation formula is used to create the pseudo analytic signal vector Z r of P x1 dimension (that has combined essence of I and Q ).
  • Z r denote the randomly measured values.
  • the one or more hardware processors 304 of the system 300 estimate a pulse-echo view by applying a compressive sensing technique over the created pseudo analytic signal vector.
  • the pulse-echo view is a vector.
  • compressive sensing refers to a technique where well-known Shanon-Nyquist data rate/sampling rate limit can be overcome for signals that have sparse representation in some domain.
  • compressive sensing technique can reconstruct original signals from very sparsely sampled data in which points are far below the usual Shanon-Nyquist rate.
  • the one or more hardware processors 304 of the system 300 first create an IFFT basis matrix ⁇ -1 for a compressive sensing computation.
  • the dimension of the IFFT basis matrix ⁇ -1 is same as number of the full frequency sweep samples or the number of frequency probing points i.e., ⁇ -1 N ⁇ N .
  • the one or more hardware processors 304 of the system 300 generate a sensing matrix A P ⁇ N by selecting one or more random rows in the IFFT basis matrix ⁇ -1 N ⁇ N .
  • the one or more hardware processors 304 of the system 300 creates a weighted diagonal matrix W by assigning one or more weights in one or more diagonal elements of the weighted diagonal matrix W.
  • the dimension of the weighted diagonal matrix W is same as the number of the full frequency sweep samples. So, the W N ⁇ N matrix is created. The creation of the weighted diagonal matrix W N ⁇ N is explained by way of following description.
  • the one or more hardware processors 304 of the system 300 first create a random sampling matrix by randomly selecting (or picking) frequencies from the IFFT basis matrix ⁇ -1 N ⁇ N .
  • the number of rows in the random sampling matrix are equal to the number of random frequencies chosen for excitation and the number of columns in the random sampling matrix are same as the dimension of the IDFT basis matrix.
  • the one or more hardware processors 304 of the system 300 then compute a pseudo-inverse of the created random sampling matrix. Thereafter, the one or more hardware processors 304 of the system 300 obtain one or more weight products by computing product of the computed pseudo-inverse with each quadrature signal of the one or more quadrature signals.
  • the one or more hardware processors 304 of the system 300 obtain one or more weights by (i) performing inversion of each of the one or more weight products to obtain one or more inverted weight products and (ii) computing absolute of the one or more inverted weight products to obtain the one or more weights. Once the one or more weights are available, the one or more hardware processors 304 of the system 300 create the weighted diagonal matrix by assigning the one or more weights in the one or more diagonal elements of the weighted diagonal matrix W.
  • the one or more hardware processors 304 of the system 300 estimate the pulse-echo view x ⁇ by solving the optimization problem using a norm minimization.
  • value of x that solves the optimization problem is considered as the pulse-echo view x ⁇ .
  • the norm minimization is an l 1 norm minimization. The norm minimization is used to obtain the sparse solution.
  • the one or more hardware processors 304 of the system 300 determine whether the estimation of the pulse-echo view is successful based on a pre-defined criteria.
  • the pre-defined criteria include checking whether x ⁇ values are lower than a predefined threshold value.
  • predefined threshold value can be 1/20th of excitation voltage. If the x ⁇ values are found to be greater than the predefined threshold value, the estimation of the pulse-echo view will be considered as successful otherwise the pulse-echo view is considered as unsuccessful.
  • the step 410 is performed otherwise new one or more new random samples are selected (e.g., such new random samples may be selected by the user of the system 300) from the plurality of samples available in the defined frequency scanning range.
  • the one or more new random samples are different from the previously selected one or more random samples.
  • the step 402 to 408 will again be performed till the estimation of the pulse-echo view will be considered as successful.
  • the one or more hardware processors 304 of the system 300 calculate the sound speed in the fluid based on the determination using the pulse-echo view and the pre-defined sound speed calculation formula.
  • the above step 410 is better understood by way of following description.
  • the one or more hardware processors 304 of the system 300 Upon determining that the estimation of the pulse-echo view is successful, the one or more hardware processors 304 of the system 300 perform normalization of magnitude of the pulse-echo view to obtain a normalized pulse-echo view. In an embodiment, the normalization is performed by dividing the entire signal x ⁇ by max ( x ⁇ ). The one or more hardware processors 304 of the system 300 then determines first two lead peaks in the normalized pulse-echo view. An example graphical representation of the pulse-echo view is shown with reference to FIG. 6 . Once the information about two lead peaks is available, the one or more hardware processors 304 of the system 300 determines time position of each of the two lead peaks. In an embodiment, the time position of first lead peak can be represented as T 1 . Similarly, the time position of second lead peak can be represented as T 2 .
  • SFAI data in TTD approach produces pulse-echo view which is sparse in nature as time pulses arrive only in certain time points. So, one can take pulse-echo view as the sparse representation of the original SFAI frequency spectra. More specifically, the time-domain view is sparse compared to the frequency domain view and thus compressive sensing can be used for this sparse representation.
  • FIG. 5 illustrates an example graphical representation of a magnitude response of a high resolution frequency sweep data, in accordance with an embodiment of the present disclosure.
  • FIG. 6 illustrates an example graphical representation of a pulse-echo view created when FFT is applied over a pseudo analytical signal, in accordance with an embodiment of the present disclosure.
  • the graphical representation of the pulse echoview shows arrival of impulses at a receiver piezo at different time instances as expected.
  • each pulse group is consisted of a lead pulse followed by shoulder pulses.
  • the shoulder pulses represent echos that took place in the wall material whereas lead pulse of each group of pulses represents pulse going back and forth in the fluid.
  • the pulse strength monotonically decreases due to attenuation of sound attenuation in materials.
  • FIG 7A is a graphical representation of a pulse-echo view created for an original high resolution spectra using standard SFAI technique, in accordance with an embodiment of the present disclosure.
  • the pulse-echo view presented in FIG. 7A is created using 6400 frequency probing points.
  • FIG 7B is a graphical representation of a reconstructed pulse-echo view for the original high resolution spectra using compressive sending based reconstruction with less number of frequency probing points, in accordance with an embodiment of the present disclosure.
  • the pulse-echo view reconstructed using 2048 frequency probing points is almost similar to the pulse-echo view created using 6400 frequency probing points.
  • the sound speed calculation from 6400 point using standard SFAI yields sound speed as 1539.6 m/s whereas the compressive SFAI with 2048 points also yields same sound speed of 1539.6 m/s.
  • the amount of I and Q data that needs to be stored is three times less than the usual data that is stored in case of standard SFAI.
  • the obtained sound is still found to be 1539.6 m/s thus making it 25 times faster than the standard SFAI while storing 25 times less I and Q data as compare to regular SFAI.
  • SFAI Swept Frequency Acoustic Interferometry
  • the SFAI uses a narrowband filtering to significantly enhance the signal quantity as compared to traditional pulse-echo based measurements.
  • SFAI technique is relatively slow in measurement as there is a need to sweep a wide range of frequencies.
  • embodiments of the present disclosure provide a method and a system for faster assessment of sound speed in fluids using compressive sensing technique. More specifically, the system assesses sound speed in liquid by applying compressive sensing technique over the standard SFAI, thus significantly reducing the frequency scanning time required to generate the SFAI data thereby making the measurement significantly faster over standard SFAI scan-based measurements.
  • the system uses a very few frequency excitation points (way below the Nyquist rate for the spectrum) and still able to reconstruct a very high resolution pulse-echo view ( x ⁇ ) as if a full high resolution frequency sweep has been performed, thereby saving significant amount of sweep time without loss of any information.
  • Such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device.
  • the hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof.
  • the device may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein.
  • the means can include both hardware means and software means.
  • the method embodiments described herein could be implemented in hardware and software.
  • the device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs.
  • the embodiments herein can comprise hardware and software elements.
  • the embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc.
  • the functions performed by various components described herein may be implemented in other components or combinations of other components.
  • a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • a computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored.
  • a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein.
  • the term "computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

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EP22197467.8A 2021-10-29 2022-09-23 Procédé et système d'évaluation plus rapide de la vitesse du son dans des fluides à l'aide d'une technique de détection de compression Pending EP4174458A1 (fr)

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Citations (3)

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US6644119B1 (en) * 2002-06-28 2003-11-11 The Regents Of The University Of California Noninvasive characterization of a flowing multiphase fluid using ultrasonic interferometry
US20170108471A1 (en) * 2013-11-26 2017-04-20 Los Alamos National Security, Llc High-temperature, high pressure acoustic resonance cell

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US5767407A (en) * 1996-01-23 1998-06-16 The Regents Of The University Of California Noninvasive identification of fluids by swept-frequency acoustic interferometry
US6644119B1 (en) * 2002-06-28 2003-11-11 The Regents Of The University Of California Noninvasive characterization of a flowing multiphase fluid using ultrasonic interferometry
US20170108471A1 (en) * 2013-11-26 2017-04-20 Los Alamos National Security, Llc High-temperature, high pressure acoustic resonance cell

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CANDES E J ET AL: "An Introduction To Compressive Sampling", IEEE SIGNAL PROCESSING MAGAZINE, IEEE, USA, vol. 25, no. 2, 1 March 2008 (2008-03-01), pages 21 - 30, XP011225660, ISSN: 1053-5888, DOI: 10.1109/MSP.2007.914731 *
JULIEN RICHY ET AL: "Blood velocity estimation using compressed sensing", ULTRASONICS SYMPOSIUM (IUS), 2011 IEEE INTERNATIONAL, IEEE, 18 October 2011 (2011-10-18), pages 1427 - 1430, XP032230377, ISBN: 978-1-4577-1253-1, DOI: 10.1109/ULTSYM.2011.0353 *
T.L. SZABO: "Diagnostic ultrasound imaging: inside out", 2004, ACADEMIC PRESS

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